Neural network-based state observation utilizing a history-of-error performance index
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Bibliographic record
Abstract
Accurate state estimation is crucial for the control and monitoring of multivariable nonlinear systems. Neural network-based observers offer promising solutions due to their universal approximation capabilities; however, maintaining precision and robustness in the presence of nonlinearities and parametric uncertainties remains a significant challenge. This paper presents an adaptive neural network observer that incorporates a history-of-error term into the weight update rules of a modified backpropagation algorithm. An e-modification term is introduced to ensure bounded state-estimation errors, with stability formally established through a Lyapunov-based analysis. Simulation and experimental studies on a one-link arm under gravity, actuated by a DC motor, demonstrate that the proposed observer can significantly enhance the estimation accuracy and convergence speed when compared to conventional neural network observers. Comparative studies indicate an approximate 50% improvement in state estimation and control accuracy, highlighting the effectiveness of the proposed approach. • Adaptive NN observer with error-history index improves accuracy, speed, convergence. • Backpropagation with e-modification guarantees bounded state estimation errors. • Stability and convergence ensured via Lyapunov-based theoretical analysis. • Achieves more than 50% improvement in state estimation vs. conventional NN observers. • Experimental evaluation confirm improved convergence and tracking performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it